Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self-Management: A Mixed...

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Chronic Disease Management

Background:

• Systemic sclerosis (SSc) requires ongoing self-management to mitigate disease progression and improve quality of life.
• Traditional self-management support may not fully address the complex needs of SSc patients.
• Emerging technologies like AI offer novel avenues for personalized patient support.

Purpose of the Study:

• To assess the utility of an AI health coach for supporting self-management in individuals with systemic sclerosis.
• To identify patterns of participant engagement with the AI health coach.
• To explore the relationship between engagement levels and self-management outcomes.

Main Methods:

• A mixed-methods study involving 20 participants with SSc interacting with an AI health coach for 4 weeks.
• Quantitative usage metrics (conversations, messages, duration) classified participants into high and low engagement groups.
• Qualitative analysis of chat transcripts identified interaction purposes, integrated with quantitative data.

Main Results:

• Participants used the AI coach for goal setting, information seeking, companionship, and monitoring.
• High engagement participants exhibited significantly more interactions related to goals, information, and companionship.
• Exploratory analysis indicated potential fatigue improvements in the high engagement group.

Conclusions:

• Quantitative usage metrics alone may not fully capture AI health coach engagement.
• Engagement is better understood through interaction frequency, action orientation, and companionship seeking.
• AI health coaches represent a potentially valuable tool for SSc self-management support.